scholarly journals Accuracy of genomic selection predictions for hip height in Brahman cattle using different relationship matrices

2018 ◽  
Vol 53 (6) ◽  
pp. 717-726 ◽  
Author(s):  
Michel Marques Farah ◽  
Marina Rufino Salinas Fortes ◽  
Matthew Kelly ◽  
Laercio Ribeiro Porto-Neto ◽  
Camila Tangari Meira ◽  
...  
Keyword(s):  
Allele Frequency ◽  
High Density ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Information ◽  
Pedigree Data ◽  
Snp Chip ◽  
Numerator Relationship Matrix ◽  
Brahman Cattle

Abstract: The objective of this work was to evaluate the effects of genomic information on the genetic evaluation of hip height in Brahman cattle using different matrices built from genomic and pedigree data. Hip height measurements from 1,695 animals, genotyped with high-density SNP chip or imputed from 50 K high-density SNP chip, were used. The numerator relationship matrix (NRM) was compared with the H matrix, which incorporated the NRM and genomic relationship (G) matrix simultaneously. The genotypes were used to estimate three versions of G: observed allele frequency (HGOF), average minor allele frequency (HGMF), and frequency of 0.5 for all markers (HG50). For matrix comparisons, animal data were either used in full or divided into calibration (80% older animals) and validation (20% younger animals) datasets. The accuracy values for the NRM, HGOF, and HG50 were 0.776, 0.813, and 0.594, respectively. The NRM and HGOF showed similar minor variances for diagonal and off-diagonal elements, as well as for estimated breeding values. The use of genomic information resulted in relationship estimates similar to those obtained based on pedigree; however, HGOF is the best option for estimating the genomic relationship matrix and results in a higher prediction accuracy. The ranking of the top 20% animals was very similar for all matrices, but the ranking within them varies depending on the method used.

scholarly journals Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Ivan Pocrnic ◽  
Daniela A. L. Lourenco ◽  
Yutaka Masuda ◽  
Ignacy Misztal
Keyword(s):  
Genomic Selection ◽  
Large Fraction ◽  
Genomic Variation ◽  
Eigenvalue Decomposition ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Information ◽  
Phenotypic Information ◽  
Largest Eigenvalues

Abstract Background The dimensionality of genomic information is limited by the number of independent chromosome segments (Me), which is a function of the effective population size. This dimensionality can be determined approximately by singular value decomposition of the gene content matrix, by eigenvalue decomposition of the genomic relationship matrix (GRM), or by the number of core animals in the algorithm for proven and young (APY) that maximizes the accuracy of genomic prediction. In the latter, core animals act as proxies to linear combinations of Me. Field studies indicate that a moderate accuracy of genomic selection is achieved with a small dataset, but that further improvement of the accuracy requires much more data. When only one quarter of the optimal number of core animals are used in the APY algorithm, the accuracy of genomic selection is only slightly below the optimal value. This suggests that genomic selection works on clusters of Me. Results The simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added. Conclusions A small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added.

0292 Dimensionality of genomic information and APY inverse of genomic relationship matrix

2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 138-139
Author(s):  
I. Pocrnic ◽  
D. A. L. Lourenco ◽  
Y. Masuda ◽  
A. Legarra ◽  
I. Misztal
Keyword(s):  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Information

scholarly journals Correction to: Validation of genomic predictions for body weight in broilers using crossbred information and considering breed-of-origin of alleles

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Pascal Duenk ◽  
Mario P. L. Calus ◽  
Yvonne C. J. Wientjes ◽  
Vivian P. Breen ◽  
John M. Henshall ◽  
...  
Keyword(s):  
Body Weight ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Predictions

Following publication of original article [1], we noticed that there was an error: Eq. (3) on page 5 is the genomic relationship matrix that

scholarly journals Application of Low Coverage Genotyping by Sequencing in Selectively Bred Arctic Charr (Salvelinus alpinus)

2020 ◽  
Vol 10 (6) ◽  
pp. 2069-2078 ◽  
Author(s):  
Christos Palaiokostas ◽  
Shannon M. Clarke ◽  
Henrik Jeuthe ◽  
Rudiger Brauning ◽  
Timothy P. Bilton ◽  
...  
Keyword(s):  
Principal Components ◽  
Salvelinus Alpinus ◽  
Arctic Charr ◽  
Genotyping By Sequencing ◽  
Breeding Value ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Value Estimation ◽  
Low Coverage

Arctic charr (Salvelinus alpinus) is a species of high economic value for the aquaculture industry, and of high ecological value due to its Holarctic distribution in both marine and freshwater environments. Novel genome sequencing approaches enable the study of population and quantitative genetic parameters even on species with limited or no prior genomic resources. Low coverage genotyping by sequencing (GBS) was applied in a selected strain of Arctic charr in Sweden originating from a landlocked freshwater population. For the needs of the current study, animals from year classes 2013 (171 animals, parental population) and 2017 (759 animals; 13 full sib families) were used as a template for identifying genome wide single nucleotide polymorphisms (SNPs). GBS libraries were constructed using the PstI and MspI restriction enzymes. Approximately 14.5K SNPs passed quality control and were used for estimating a genomic relationship matrix. Thereafter a wide range of analyses were conducted in order to gain insights regarding genetic diversity and investigate the efficiency of the genomic information for parentage assignment and breeding value estimation. Heterozygosity estimates for both year classes suggested a slight excess of heterozygotes. Furthermore, FST estimates among the families of year class 2017 ranged between 0.009 – 0.066. Principal components analysis (PCA) and discriminant analysis of principal components (DAPC) were applied aiming to identify the existence of genetic clusters among the studied population. Results obtained were in accordance with pedigree records allowing the identification of individual families. Additionally, DNA parentage verification was performed, with results in accordance with the pedigree records with the exception of a putative dam where full sib genotypes suggested a potential recording error. Breeding value estimation for juvenile growth through the usage of the estimated genomic relationship matrix clearly outperformed the pedigree equivalent in terms of prediction accuracy (0.51 opposed to 0.31). Overall, low coverage GBS has proven to be a cost-effective genotyping platform that is expected to boost the selection efficiency of the Arctic charr breeding program.

scholarly journals Efficient Algorithms for Calculating Epistatic Genomic Relationship Matrices

Genetics ◽  
2020 ◽  
Vol 216 (3) ◽  
pp. 651-669
Author(s):  
Yong Jiang ◽  
Jochen C. Reif
Keyword(s):  
Hadamard Product ◽  
Association Studies ◽  
Complete Solution ◽  
Natural Generalization ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genome Wide Association Studies ◽  
Genomic Relationship ◽  
Genome Wide ◽  
High Degree

The genomic relationship matrix plays a key role in the analysis of genetic diversity, genomic prediction, and genome-wide association studies. The epistatic genomic relationship matrix is a natural generalization of the classic genomic relationship matrix in the sense that it implicitly models the epistatic effects among all markers. Calculating the exact form of the epistatic relationship matrix requires high computational load, and is hence not feasible when the number of markers is large, or when high-degree of epistasis is in consideration. Currently, many studies use the Hadamard product of the classic genomic relationship matrix as an approximation. However, the quality of the approximation is difficult to investigate in the strict mathematical sense. In this study, we derived iterative formulas for the precise form of the epistatic genomic relationship matrix for arbitrary degree of epistasis including both additive and dominance interactions. The key to our theoretical results is the observation of an interesting link between the elements in the genomic relationship matrix and symmetric polynomials, which motivated the application of the corresponding mathematical theory. Based on the iterative formulas, efficient recursive algorithms were implemented. Compared with the approximation by the Hadamard product, our algorithms provided a complete solution to the problem of calculating the exact epistatic genomic relationship matrix. As an application, we showed that our new algorithms easily relieved the computational burden in a previous study on the approximation behavior of two limit models.

scholarly journals Forensic use of the genomic relationship matrix to validate and discover livestock pedigrees

2018 ◽  
Vol 97 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Kirsty Lee Moore ◽  
Conrad Vilela ◽  
Karolina Kaseja ◽  
Raphael Mrode ◽  
Mike Coffey
Keyword(s):  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship
Sign in / Sign up

Export Citation Format

Share Document